Statistics Assessment

Learning outcomes (LO)

The following learning outcomes are applicable to all Math majors, independently of the program.

Proficiency in Statistical Methods: Students should demonstrate a deep understanding of various statistical methods, including descriptive statistics, inferential statistics, regression analysis, hypothesis testing, and multivariate analysis. They should be able to apply these methods to analyze real-world data sets and draw meaningful conclusions.
Data Analysis Skills: Students should develop strong skills in data collection, data cleaning, and data visualization techniques. They should be proficient in using statistical software packages such as R, Python, or SAS to manipulate and analyze data effectively.
Critical Thinking and Problem-Solving Abilities: Students should be able to critically evaluate statistical methods and models, assess their appropriateness for different scenarios, and make informed decisions based on statistical evidence. They should be adept at identifying patterns, trends, and outliers in data and proposing solutions to complex problems.
Statistical Theory: Students will demonstrate proficiency in the theoretical underpinnings of probability and statistical inference.
Communication Skills: Students should be capable of communicating statistical concepts and findings clearly and effectively to both technical and non-technical audiences. This includes writing reports, creating visualizations, and delivering presentations that convey the results of statistical analyses in a coherent and understandable manner.

As per the General Catalog of the University, the following Stats major programs share common courses within their respective "Major Requirements":

The common courses listed below are used for the evaluation purposes.

STAT 3000 - Statistics for Scientists

STAT 5100 - Modern Regression Methods

STAT 5200 - Analysis of Designed Experiments

MATH 5710 - Introduction to Probability

MATH 5720 - Introduction to Mathematical Statistics